Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations4177
Missing cells248
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory293.8 KiB
Average record size in memory72.0 B

Variable types

Categorical1
Numeric8

Alerts

Diameter is highly overall correlated with Height and 6 other fieldsHigh correlation
Height is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Length is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Rings is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Shell weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Shucked weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Viscera weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Whole weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Diameter has 99 (2.4%) missing values Missing
Whole weight has 99 (2.4%) missing values Missing
Shell weight has 50 (1.2%) missing values Missing

Reproduction

Analysis started2024-12-31 06:35:36.390894
Analysis finished2024-12-31 06:36:14.530241
Duration38.14 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Sex
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.8 KiB
M
1447 
I
1276 
F
1259 
f
195 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4177
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowF
4th rowM
5th rowI

Common Values

ValueCountFrequency (%)
M 1447
34.6%
I 1276
30.5%
F 1259
30.1%
f 195
 
4.7%

Length

2024-12-31T06:36:14.760197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-31T06:36:15.116350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
f 1454
34.8%
m 1447
34.6%
i 1276
30.5%

Most occurring characters

ValueCountFrequency (%)
M 1447
34.6%
I 1276
30.5%
F 1259
30.1%
f 195
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1447
34.6%
I 1276
30.5%
F 1259
30.1%
f 195
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1447
34.6%
I 1276
30.5%
F 1259
30.1%
f 195
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1447
34.6%
I 1276
30.5%
F 1259
30.1%
f 195
 
4.7%

Length
Real number (ℝ)

High correlation 

Distinct134
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5239921
Minimum0.075
Maximum0.815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-31T06:36:15.434834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.075
5-th percentile0.295
Q10.45
median0.545
Q30.615
95-th percentile0.69
Maximum0.815
Range0.74
Interquartile range (IQR)0.165

Descriptive statistics

Standard deviation0.12009291
Coefficient of variation (CV)0.2291884
Kurtosis0.064620974
Mean0.5239921
Median Absolute Deviation (MAD)0.08
Skewness-0.63987327
Sum2188.715
Variance0.014422308
MonotonicityNot monotonic
2024-12-31T06:36:15.797952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.625 94
 
2.3%
0.55 94
 
2.3%
0.575 93
 
2.2%
0.58 92
 
2.2%
0.6 87
 
2.1%
0.62 87
 
2.1%
0.5 81
 
1.9%
0.57 79
 
1.9%
0.63 78
 
1.9%
0.61 75
 
1.8%
Other values (124) 3317
79.4%
ValueCountFrequency (%)
0.075 1
 
< 0.1%
0.11 1
 
< 0.1%
0.13 2
 
< 0.1%
0.135 1
 
< 0.1%
0.14 2
 
< 0.1%
0.15 1
 
< 0.1%
0.155 3
0.1%
0.16 4
0.1%
0.165 5
0.1%
0.17 3
0.1%
ValueCountFrequency (%)
0.815 1
 
< 0.1%
0.8 1
 
< 0.1%
0.78 2
 
< 0.1%
0.775 2
 
< 0.1%
0.77 3
 
0.1%
0.765 2
 
< 0.1%
0.76 2
 
< 0.1%
0.755 3
 
0.1%
0.75 8
0.2%
0.745 5
0.1%

Diameter
Real number (ℝ)

High correlation  Missing 

Distinct111
Distinct (%)2.7%
Missing99
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean0.40783963
Minimum0.055
Maximum0.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-31T06:36:16.124707image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.055
5-th percentile0.22
Q10.35
median0.425
Q30.48
95-th percentile0.545
Maximum0.65
Range0.595
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.099286042
Coefficient of variation (CV)0.24344383
Kurtosis-0.051005104
Mean0.40783963
Median Absolute Deviation (MAD)0.065
Skewness-0.60520317
Sum1663.17
Variance0.0098577182
MonotonicityNot monotonic
2024-12-31T06:36:16.466531image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45 137
 
3.3%
0.475 117
 
2.8%
0.4 107
 
2.6%
0.5 105
 
2.5%
0.47 97
 
2.3%
0.48 90
 
2.2%
0.46 88
 
2.1%
0.455 86
 
2.1%
0.44 86
 
2.1%
0.375 80
 
1.9%
Other values (101) 3085
73.9%
(Missing) 99
 
2.4%
ValueCountFrequency (%)
0.055 1
 
< 0.1%
0.09 1
 
< 0.1%
0.095 1
 
< 0.1%
0.1 2
 
< 0.1%
0.105 4
0.1%
0.11 4
0.1%
0.115 2
 
< 0.1%
0.12 4
0.1%
0.125 7
0.2%
0.13 8
0.2%
ValueCountFrequency (%)
0.65 1
 
< 0.1%
0.63 3
0.1%
0.625 1
 
< 0.1%
0.62 1
 
< 0.1%
0.615 1
 
< 0.1%
0.61 1
 
< 0.1%
0.605 3
0.1%
0.6 7
0.2%
0.595 4
0.1%
0.59 6
0.1%

Height
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1395164
Minimum0
Maximum1.13
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-31T06:36:16.826648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.075
Q10.115
median0.14
Q30.165
95-th percentile0.2
Maximum1.13
Range1.13
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.041827057
Coefficient of variation (CV)0.29980029
Kurtosis76.025509
Mean0.1395164
Median Absolute Deviation (MAD)0.025
Skewness3.1288174
Sum582.76
Variance0.0017495027
MonotonicityNot monotonic
2024-12-31T06:36:17.162269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15 267
 
6.4%
0.14 220
 
5.3%
0.155 217
 
5.2%
0.175 211
 
5.1%
0.16 205
 
4.9%
0.125 202
 
4.8%
0.165 193
 
4.6%
0.135 189
 
4.5%
0.145 182
 
4.4%
0.13 169
 
4.0%
Other values (41) 2122
50.8%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.01 1
 
< 0.1%
0.015 2
 
< 0.1%
0.02 2
 
< 0.1%
0.025 5
 
0.1%
0.03 6
 
0.1%
0.035 6
 
0.1%
0.04 13
0.3%
0.045 11
0.3%
0.05 18
0.4%
ValueCountFrequency (%)
1.13 1
 
< 0.1%
0.515 1
 
< 0.1%
0.25 3
 
0.1%
0.24 4
 
0.1%
0.235 6
 
0.1%
0.23 10
 
0.2%
0.225 13
0.3%
0.22 17
0.4%
0.215 31
0.7%
0.21 23
0.6%

Whole weight
Real number (ℝ)

High correlation  Missing 

Distinct2391
Distinct (%)58.6%
Missing99
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean0.82730579
Minimum0.002
Maximum2.8255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-31T06:36:17.482610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.124925
Q10.4405
median0.7985
Q31.150875
95-th percentile1.6918
Maximum2.8255
Range2.8235
Interquartile range (IQR)0.710375

Descriptive statistics

Standard deviation0.49034787
Coefficient of variation (CV)0.59270451
Kurtosis-0.03230349
Mean0.82730579
Median Absolute Deviation (MAD)0.3565
Skewness0.52897211
Sum3373.753
Variance0.24044104
MonotonicityNot monotonic
2024-12-31T06:36:17.985026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2225 8
 
0.2%
1.1345 7
 
0.2%
0.4775 7
 
0.2%
0.196 7
 
0.2%
0.18 6
 
0.1%
0.5805 6
 
0.1%
0.494 6
 
0.1%
0.3245 6
 
0.1%
0.97 6
 
0.1%
0.6765 6
 
0.1%
Other values (2381) 4013
96.1%
(Missing) 99
 
2.4%
ValueCountFrequency (%)
0.002 1
< 0.1%
0.008 1
< 0.1%
0.0105 1
< 0.1%
0.013 1
< 0.1%
0.014 1
< 0.1%
0.0145 2
< 0.1%
0.015 1
< 0.1%
0.0155 1
< 0.1%
0.0175 1
< 0.1%
0.018 2
< 0.1%
ValueCountFrequency (%)
2.8255 1
< 0.1%
2.7795 1
< 0.1%
2.657 1
< 0.1%
2.555 1
< 0.1%
2.55 1
< 0.1%
2.526 1
< 0.1%
2.5155 1
< 0.1%
2.5085 1
< 0.1%
2.505 1
< 0.1%
2.499 1
< 0.1%

Shucked weight
Real number (ℝ)

High correlation 

Distinct1515
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35936749
Minimum0.001
Maximum1.488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-31T06:36:18.511599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.0524
Q10.186
median0.336
Q30.502
95-th percentile0.7402
Maximum1.488
Range1.487
Interquartile range (IQR)0.316

Descriptive statistics

Standard deviation0.22196295
Coefficient of variation (CV)0.61764894
Kurtosis0.59512368
Mean0.35936749
Median Absolute Deviation (MAD)0.1585
Skewness0.71909792
Sum1501.078
Variance0.049267551
MonotonicityNot monotonic
2024-12-31T06:36:19.192509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.175 11
 
0.3%
0.2505 10
 
0.2%
0.097 9
 
0.2%
0.096 9
 
0.2%
0.419 9
 
0.2%
0.302 9
 
0.2%
0.2 9
 
0.2%
0.165 9
 
0.2%
0.21 9
 
0.2%
0.2945 9
 
0.2%
Other values (1505) 4084
97.8%
ValueCountFrequency (%)
0.001 1
 
< 0.1%
0.0025 1
 
< 0.1%
0.0045 2
< 0.1%
0.005 3
0.1%
0.0055 2
< 0.1%
0.0065 3
0.1%
0.007 1
 
< 0.1%
0.0075 4
0.1%
0.008 1
 
< 0.1%
0.0085 1
 
< 0.1%
ValueCountFrequency (%)
1.488 1
< 0.1%
1.351 1
< 0.1%
1.3485 1
< 0.1%
1.253 1
< 0.1%
1.2455 1
< 0.1%
1.2395 2
< 0.1%
1.232 1
< 0.1%
1.1965 1
< 0.1%
1.1945 1
< 0.1%
1.1705 1
< 0.1%

Viscera weight
Real number (ℝ)

High correlation 

Distinct880
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18059361
Minimum0.0005
Maximum0.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-31T06:36:19.841128image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.027
Q10.0935
median0.171
Q30.253
95-th percentile0.3796
Maximum0.76
Range0.7595
Interquartile range (IQR)0.1595

Descriptive statistics

Standard deviation0.10961425
Coefficient of variation (CV)0.60696639
Kurtosis0.084011749
Mean0.18059361
Median Absolute Deviation (MAD)0.0795
Skewness0.59185215
Sum754.3395
Variance0.012015284
MonotonicityNot monotonic
2024-12-31T06:36:20.448668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1715 15
 
0.4%
0.196 14
 
0.3%
0.0575 13
 
0.3%
0.061 13
 
0.3%
0.037 13
 
0.3%
0.2195 13
 
0.3%
0.159 12
 
0.3%
0.1625 12
 
0.3%
0.0265 12
 
0.3%
0.207 12
 
0.3%
Other values (870) 4048
96.9%
ValueCountFrequency (%)
0.0005 2
 
< 0.1%
0.002 1
 
< 0.1%
0.0025 2
 
< 0.1%
0.003 3
0.1%
0.0035 3
0.1%
0.004 1
 
< 0.1%
0.0045 4
0.1%
0.005 7
0.2%
0.0055 6
0.1%
0.006 2
 
< 0.1%
ValueCountFrequency (%)
0.76 1
< 0.1%
0.6415 1
< 0.1%
0.59 1
< 0.1%
0.575 1
< 0.1%
0.5745 1
< 0.1%
0.564 1
< 0.1%
0.55 1
< 0.1%
0.541 2
< 0.1%
0.5265 1
< 0.1%
0.526 1
< 0.1%

Shell weight
Real number (ℝ)

High correlation  Missing 

Distinct919
Distinct (%)22.3%
Missing50
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.2390716
Minimum0.0015
Maximum1.005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-31T06:36:21.136692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.039
Q10.13
median0.235
Q30.32825
95-th percentile0.48
Maximum1.005
Range1.0035
Interquartile range (IQR)0.19825

Descriptive statistics

Standard deviation0.13894199
Coefficient of variation (CV)0.58117312
Kurtosis0.54956652
Mean0.2390716
Median Absolute Deviation (MAD)0.1
Skewness0.62275267
Sum986.6485
Variance0.019304876
MonotonicityNot monotonic
2024-12-31T06:36:22.313492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.275 43
 
1.0%
0.25 42
 
1.0%
0.315 40
 
1.0%
0.265 40
 
1.0%
0.185 40
 
1.0%
0.285 37
 
0.9%
0.17 36
 
0.9%
0.175 36
 
0.9%
0.3 36
 
0.9%
0.07 35
 
0.8%
Other values (909) 3742
89.6%
(Missing) 50
 
1.2%
ValueCountFrequency (%)
0.0015 1
 
< 0.1%
0.003 1
 
< 0.1%
0.0035 1
 
< 0.1%
0.004 2
 
< 0.1%
0.005 12
0.3%
0.006 1
 
< 0.1%
0.0065 1
 
< 0.1%
0.007 1
 
< 0.1%
0.0075 1
 
< 0.1%
0.008 4
 
0.1%
ValueCountFrequency (%)
1.005 1
 
< 0.1%
0.897 1
 
< 0.1%
0.885 2
< 0.1%
0.85 1
 
< 0.1%
0.815 1
 
< 0.1%
0.7975 1
 
< 0.1%
0.78 1
 
< 0.1%
0.76 1
 
< 0.1%
0.726 1
 
< 0.1%
0.725 3
0.1%

Rings
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.433684
Minimum2.5
Maximum30.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-31T06:36:22.922554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile7.5
Q19.5
median10.5
Q312.5
95-th percentile17.5
Maximum30.5
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.224169
Coefficient of variation (CV)0.28198863
Kurtosis2.3306874
Mean11.433684
Median Absolute Deviation (MAD)2
Skewness1.1141019
Sum47758.5
Variance10.395266
MonotonicityNot monotonic
2024-12-31T06:36:23.421799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
10.5 689
16.5%
11.5 634
15.2%
9.5 568
13.6%
12.5 487
11.7%
8.5 391
9.4%
13.5 267
 
6.4%
7.5 259
 
6.2%
14.5 203
 
4.9%
15.5 126
 
3.0%
6.5 115
 
2.8%
Other values (18) 438
10.5%
ValueCountFrequency (%)
2.5 1
 
< 0.1%
3.5 1
 
< 0.1%
4.5 15
 
0.4%
5.5 57
 
1.4%
6.5 115
 
2.8%
7.5 259
 
6.2%
8.5 391
9.4%
9.5 568
13.6%
10.5 689
16.5%
11.5 634
15.2%
ValueCountFrequency (%)
30.5 1
 
< 0.1%
28.5 2
 
< 0.1%
27.5 1
 
< 0.1%
26.5 1
 
< 0.1%
25.5 2
 
< 0.1%
24.5 9
 
0.2%
23.5 6
 
0.1%
22.5 14
0.3%
21.5 26
0.6%
20.5 32
0.8%

Interactions

2024-12-31T06:36:11.323961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:37.269295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:42.080640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:49.647387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:54.098380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:58.121974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:01.829195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:09.001381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:11.586068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:38.061043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:42.734133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:50.646097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:54.519064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:58.618135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:02.493655image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:09.389065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:11.844715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:38.498674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:43.212626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:51.377355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:54.856852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:59.099882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:03.421886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:09.622640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:12.110030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:39.264362image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:44.357252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:51.957396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:55.660464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:59.604066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:04.507465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:09.854019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:12.381492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:39.788753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:45.495561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:52.453064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:56.090909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:00.188506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:06.107428image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:10.106884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:12.638176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:40.271526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:46.582376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:52.930842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:56.578258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:00.531549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:07.354639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:10.365909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:12.913683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:40.875303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:47.533337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:53.322059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:57.072086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:00.962147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:08.158814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:10.606793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:13.163970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:41.458920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:48.433584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:53.689065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:35:57.548891image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:01.362688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:08.638757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-31T06:36:10.840123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-31T06:36:23.648442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
DiameterHeightLengthRingsSexShell weightShucked weightViscera weightWhole weight
Diameter1.0000.8960.9830.6210.3210.9530.9500.9480.971
Height0.8961.0000.8880.6580.2870.9210.8740.9010.917
Length0.9830.8881.0000.6040.3140.9480.9570.9530.973
Rings0.6210.6580.6041.0000.2840.6900.5390.6140.631
Sex0.3210.2870.3140.2841.0000.3290.3130.3380.338
Shell weight0.9530.9210.9480.6900.3291.0000.9170.9380.969
Shucked weight0.9500.8740.9570.5390.3130.9171.0000.9480.977
Viscera weight0.9480.9010.9530.6140.3380.9380.9481.0000.975
Whole weight0.9710.9170.9730.6310.3380.9690.9770.9751.000

Missing values

2024-12-31T06:36:13.513883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-31T06:36:13.931591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-31T06:36:14.350324image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SexLengthDiameterHeightWhole weightShucked weightViscera weightShell weightRings
0M0.4550.3650.0950.51400.22450.10100.15016.5
1M0.3500.2650.0900.22550.09950.04850.0708.5
2F0.5300.4200.1350.67700.25650.14150.21010.5
3M0.4400.3650.125NaN0.21550.11400.15511.5
4I0.3300.2550.0800.20500.08950.03950.0558.5
5I0.4250.3000.0950.35150.14100.07750.1209.5
6F0.5300.4150.1500.77750.23700.14150.33021.5
7F0.5450.4250.1250.76800.29400.14950.26017.5
8M0.4750.3700.1250.50950.21650.11250.16510.5
9F0.5500.4400.1500.89450.31450.15100.32020.5
SexLengthDiameterHeightWhole weightShucked weightViscera weightShell weightRings
4167M0.5000.3800.1250.57700.26900.12650.153510.5
4168F0.5150.4000.1250.61500.28650.12300.17659.5
4169M0.5200.3850.1650.79100.37500.18000.181511.5
4170M0.5500.4300.1300.83950.31550.19550.240511.5
4171M0.5600.4300.1550.86750.40000.17200.22909.5
4172F0.5650.4500.1650.88700.37000.23900.249012.5
4173M0.5900.4400.1350.96600.43900.21450.260511.5
4174M0.6000.4750.2051.17600.52550.28750.308010.5
4175F0.6250.4850.1501.09450.53100.26100.296011.5
4176M0.7100.5550.1951.94850.94550.37650.495013.5